As the integration of Artificial Intelligence with the real economy deepens, AI has emerged as a pivotal engine driving the transformation of the digital economy and a cornerstone for building core corporate competitiveness. It also serves as a vital catalyst for enterprises across various sectors to accelerate technological self-reliance, foster self-strengthening, and promote high-quality development.
According to PwC’s Global AI Performance Study, intense AI adoption has not yet translated into measurable returns for many enterprises. At this stage, value remains highly concentrated among a select few—of the 1,217 global companies surveyed, a mere 20% of “AI leaders” captured 74% of the economic gains driven by AI technology.
By benchmarking the AI fitness indices of AI leaders, Chinese enterprises, and others, our report dissects the performance growth achieved by leading companies through AI adoption, and simultaneously analyses the competitive advantages and systemic bottlenecks inherent in the AI development journey of Chinese enterprises. Drawing upon the winning strategies of global leaders, this report offers actionable recommendations to help enterprises better capitalise on AI to drive sustainable revenue growth.
The 15th Five-Year Plan calls for the comprehensive implementation of the AI-plus initiative, strengthening underlying supply through the integration of computing power, algorithms, and data, while accelerating independent innovation in key technologies and full-industry scenario penetration. As domestic digital-supporting institutions continue to improve and enterprises gradually close gaps in AI governance and technical foundations, Chinese enterprises are expected to achieve AI performance levels fully on par with global top-tier AI leaders in the future.
Key findings for Chinese enterprises
Chinese enterprises lead the world in AI fitness, outperforming global AI leaders on most indicators.
Strong advantage in industry convergence: Chinese enterprises exhibit significantly higher penetration of AI applications related to sector convergence—including cross-enterprise collaboration, responsiveness to shifts in customer needs, and unlocking new value from cross-sector ecosystems—than global AI leaders. In particular, the penetration rate of cross-sector collaboration is approximately 2.3 times higher than that of enterprises globally.
Superior execution-layer implementation efficiency: Execution-oriented AI applications, such as process automation and replacement of standardised operations, slightly outperform those of global AI leaders. Chinese enterprises demonstrate faster AI deployment and stronger execution capabilities in scaling and rollout.
Gaps remain in closing the value loop: Although most enterprises have begun to focus on the actual business value of AI and have established strategic plans that combine short- and long-term horizons, their capabilities in systematically tracking AI’s business impact and executing strategy still lag significantly behind global AI leaders.
Lower efficiency in converting innovation into value: Investment in innovation experimentation infrastructure exceeds that of global AI leaders, yet there is a lack of mechanisms to expand from individual pilots to large-scale rollout. As a result, innovation outcomes tend to remain confined to the pilot stage, limiting the long-term compounding returns on investment. The phenomenon of “pilots being relatively easy, while scaling and full implementation remain difficult” continues to be widespread.
Trust and governance require further strengthening: The foundation of enterprise trust in AI remains relatively weak. Employees generally remain cautious about acting on AI-generated insights and have not yet integrated them into day-to-day decision-making to the same extent as global AI leaders. At the same time, most enterprises have yet to meet the regulatory standards and compliance frameworks demonstrated by industry benchmarks. Clear room for improvement exists in both willingness to apply AI and compliance capabilities.
Development insights and outlook
Complete the strategic value loop and improve return on investment efficiency
Enterprises may refer to the “monthly business value review” mechanism commonly adopted by AI leaders, which involves regular assessment of AI project value progress, termination of pilot projects that show no clear value within six months, and reallocation of resources toward higher-value initiatives.
Strengthen long-term innovation investment and optimise resource allocation mechanisms
Enterprises need to maintain current investment intensity while appropriately increasing the proportion of resources allocated to long-term, innovation-oriented AI projects. At the same time, they need to establish flexible dynamic resource scheduling mechanisms that enable rapid reallocation of funds and talent to high-value, growth-oriented AI projects as business priorities shift.
Tackle high-tier application scenarios to unlock growth potential
Drawing on the practices of AI leaders, companies need to launch 2–3 AI-driven pilot projects each year with clearly defined performance standards for implementation outcomes. In parallel, they need to develop benchmark demonstrations of high-tier AI applications, select priority scenarios, and explore deployment pathways for AI in complex decision-making and dynamic optimisation scenarios.
Focus on scenario-driven optimisation of the technical foundation and data to avoid blind transformation
Advancing key data governance initiatives such as data lineage tracing, data asset inventory, and data quality improvement. By adopting the “scenario-oriented” foundation-building logic commonly used by global AI leaders, companies can achieve large-scale AI deployment at lower cost.
Improve AI culture and supporting mechanisms to increase trust and adoption rates
Enterprises could establish protective mechanisms and operational guidelines to further build employee confidence. When people clearly understand the authorised scope of AI use, situations requiring escalation, and accountability structures, they can apply the technology with greater confidence.
Strengthen governance and compliance systems to balance innovation and risk
Establishing cross-functional AI governance committees involving business, technology, and compliance teams, and implement AI frameworks that meet domestic regulatory requirements. Compliance reviews need to be conducted prior to the deployment of high-risk AI use cases. Standardised templates and lightweight review processes can be adopted to balance governance requirements with deployment efficiency.
“Artificial intelligence is reshaping the global business landscape. Our 2026 Global AI Performance Study clearly shows that systematic AI deployment can deliver significant value for enterprises. China’s AI application ecosystem is vibrant and dynamic, demonstrating remarkable progress in cross-industry integration, with implementation practices that rank amongst the most advanced globally. As China’s AI market continues to develop, it is giving rise to a wide range of innovative models that can offer valuable insights for the global AI industry, support the digital transformation of the real economy, and contribute to the development of the global digital economy.”
— Hemione Hudson
Chair and CEO, PwC China
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